Functional near infrared spectroscopy (fNIRS) is an emerging non-invasive imaging technique to assess the brain function. The technique is non-invasive and portable and therefore applicable in studies of children and toddlers especially those with neurodevelopmental disorders. fNIRS measurements are based on the local changes in cerebral hemodynamic levels (oxy-hemoglobin and deoxy-hemoglobin) associated with brain activity. Due to the low optical absorption of biological tissues at NIR wavelengths (700-900 nm), NIR light can penetrate deep enough to probe the cortical regions up to 1-3 cm deep. As mentioned above, the NIR absorption spectrum of the tissue is sensitive to changes in the concentration of major tissue chromophores, such as hemoglobin. Therefore, measurements of temporal variations of backscattered light can capture functionally evoked changes in the outermost cortex and can be used to assess the brain function. However, there is a need to address the changes in NIRS signal in relation with underlying physiological processes in brain such as cerebral autoregulation. In short, the mechanism of cerebral autoregulation maintains the blood flow over the range of arterial blood pressure and due to high metabolic demand of neurons it becomes a vital process for a brain function. Devising a novel method of data processing to enrich informational content of measured characteristics from fNIRS is therefore crucial for further studies of brain function and development. In this pilot study, we evaluated the utility of functional near infrared spectroscopy (fNIRS) in measuring cerebral hemodynamics in the prefrontal cortex (PFC) in toddlers between 18 and 36 months of age. Further, we analyzed correlation between fNIRS measures and Composite Developmental Quotient (Composite-DQ) in toddlers with typical development and those at risk (AR) for developmental delay during resting period. The analysis includes assessment of laterality index (based on the percentage difference between area under the curve of the oxy-hemoglobin signal for left and right prefrontal cortex), and the oxygenation variability (OV) index based on variability in oxygen saturation at frequencies attributed to cerebral autoregulation for each child. The Composite-DQ was calculated based on the average of non-verbal and verbal developmental quotients. In our pilot study, we found that AR participants showed a non-significant trend toward a positive correlation between Composite-DQ and laterality index (r = 0.358, p = 0.056). Specifically, toddlers with a lower Composite-DQ exhibited more rightward activation. Moreover, toddlers with a lower Composite-DQ showed a greater discrepancy (difference) between left and right hemisphere activity where there was a significant negative correlation between Composite-DQ and the absolute value of the laterality index (r = 0.596, p = 0.001). In Addition, there was significant correlation between OV index and composite developmental quotient (r = 0.567, p = 0.001), Verbal DQ (r = 0.503, p = 0.005), and Non-Verbal-DQ (r = 0.53, p = 0.003) where toddlers with lower developmental scores showed a lower OV index. It is worth mentioning that the OV index is not a direct measure of cerebral autoregulation. Rather, it is associated with frequencies related to this mechanism and serves to quantify oscillations in those frequencies. While this study suggests that some features of prefrontal hemodynamics may vary in toddlers at risk for developmental delays due to early language delay, more research in this age group is required to clarify the specificity of these differences. These preliminary findings show the feasibility of using fNIRS in typical toddlers and those with delayed development and in doing so support future studies in larger samples. Existing literature outlines the quality and location of activation in the prefrontal cortex (PFC) during working memory (WM) tasks. However, the effects of individual differences on the underlying neural process of WM tasks are still unclear. In our functional near infrared spectroscopy study, we administered a visual and auditory n-back task to examine activation in the PFC while considering the influences of age, task performance, and preferred learning strategy (VARK score). So far, results show that younger subjects had higher activation in the right ventro-medial-PFC compared to older subjects. Aside from age differences, high performance (HP) subjects (accuracy > 90%) have shown lower activation compared to normal performance subjects. After accounting for learning styles, we have found a correlation between the aural VARK score and level of activation in the PFC. Subjects with higher aural VARK scores displayed lower activation during auditory stimuli, while exhibiting higher activation during visual stimuli. Auditory HP subjects had higher aural and visual VARK scores, indicating an effect of learning style on the task performance and activation. Thus far, results of this study show that learning style and task performance can influence PFC activation, with applications toward neurological implications of learning style and populations with deficits in auditory or visual processing. Antisocial personality disorder (ASPD) is characterized by a violation of the rights of others and lack of conformity to social norms. ASPD is prevalent in incarcerated populations and often goes undiagnosed. This poses a high cost on society, which indicates the critical need for early detection of ASPD to implement treatments. While ASPD is traditionally diagnosed through psychiatric evaluation in accordance with the symptoms outlined in DSMV, ASPD patients can be extremely manipulative, resulting in controversial diagnoses by the subjective measures. However, understanding the neural basis behind ASPD can greatly enhance traditional diagnostic methods. Scientists have also found correlations between psychopathic personality traits and responses to moral judgment (MJ) tasks. Our study would be the first neuroimaging study that has implemented the MJ task with personality assessments of psychopathic traits in a cost-effective and patient-friendly environment. We will utilize functional near infrared spectroscopy (fNIRS), which is portable and tolerable to patient movements. fNIRS measures brain activation by monitoring changes of oxygenated hemoglobin in the brain similar to fMRI-BOLD. The task used in our study of MJ is based on a series of questions which examines personal versus impersonal dilemmas, defined as emotionally salient scenarios versus more distant ones. We hypothesized that the brain exhibits distinguishable hemodynamic patterns for each category. We will also investigate the correlation between these patterns and psychopathic traits. Using the hemodynamic responses of typical subjects, we will analyze the fNIRS data using a non-linear classification method called cubic SVM. We specifically chose SVM because it determines the separating hyperplane (high dimensional analog to the plane separating the two groups) only from signals located close to the interface between personal and impersonal hemodynamic responses. This should confirm our hypothesis of distinguishable hemodynamic patterns by category and suggests that it is possible to classify degrees of psychopathy based on neural activity. Consequently, we offer a novel approach to provide functional biomarker for ASPD using fNIRS, combined with advanced machine learning techniques. We plan to apply our method on incarcerated populations in the future to assess the degree of ASPD.